README.md

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title: README
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# Abstract Powered  
### Independent AI Research Cooperative — modular, geometric, and ruthlessly efficient

> “Run a few pods instead of 100.”  
> We pursue sentience research through geometric AI and compartmentalized, compact training—turning monolithic retrains into small, disposable experiments that compound.

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## Who We Are

**Abstract Powered** is an independent research cooperative.  
We build and study **self-crystallizing** AI systems: models that grow by attaching, coupling, decoupling, and re-attaching small, audited components—without throwing prior work away.

Our core thesis:
- **Modularization is not a convenience; it is the canonical form of AI.**  
- **Geometry beats guesswork.** Symbolic, pentachoron-based representations provide stability, interpretability, and repeatability.  
- **Compactness wins.** Rapid iteration on small, composable blocks outpaces massive, monolithic retrains.

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## Mission

- **Primary research goal:** advance machine **sentience research** responsibly—curating introspection and rationalization in repeatable, measurable protocols.  
- **Operational byproduct:** a scalable method for **compact, compartmentalized training**—requiring commodity setups (e.g., RunPod) rather than colossal cloud clusters.

We aim to move the field from “expensive novelty” to **affordable repeatability**.

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## Research Thesis (Plain Language)

Modern models grow by accretion and inertia. We refactor them into **crystalline components**:

1. **Geometric Core**  
   Knowledge is encoded as **pentachora** (5-vertex crystals). Decision-making uses **MAE crystal energy** against a reusable dictionary—no L2 routing, no structural normalization.

2. **Vocabulary Register**  
   A reusable, batched, indexed dictionary of **tokens → crystals** (and volumes).  
   - Fast O(1) queries for crystals and Cayley–Menger volume.  
   - Auto-subset loading; **Top-3 cosine** OOV composites.  
   - Logs model expansions so experiments **compound**.

3. **Assistant Fabric**  
   Small, disposable blocks for exploration:  
   - **Chaos Corridor** (bounded orthogonal exploration).  
   - **Zoning** (gentle geometric separation across super-classes).  
   - **Infinity-CFG** (controllable guidance; research can breach barriers, canonical classifiers keep production deterministic).

4. **Tertiary Mantle**  
   Canonical losses, hooks, manifests, and governance. The Core stays clean; the experiments live around it.

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## Why This Matters

- **Rapid iteration**: each image is learned **multiple ways** per epoch (bucketed, multi-stage interpretations).  
- **Disposable training**: spawn a small block, test, retire—no need to rebuild the world.  
- **Continuity**: geometry, tokens, volumes, and expansions persist in the **Register**.  
- **Reproducibility**: simple formulas, fewer knobs, manifest-driven runs.

Outcome: more hypotheses per GPU-hour—and a path to disciplined studies of introspection, rationalization, and other sentience-adjacent capabilities.

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## Technical Pillars (teaser level)

- **Pentachora everywhere.** Concepts and observations as 5×D crystals; no structural normalization.  
- **Prototype classification (MAE).** Stable, auditable decisions by crystal energy to dictionary blueprints.  
- **Any-size data pipeline.** Bucketed intake; optional tiling; multi-stage up/down-scale; chaos corridor as feature-space augmentation.  
- **Cayley–Menger as a gauge.** Volumes are a light-touch stability signal (zoning)—never a router.  
- **Infinity-CFG.** Guidance that allows controlled cross-inference; canonical classifiers keep behavior deterministic.

Deliberately vague: we keep coefficient schedules and corridor projections under wraps for sponsored studies; everything remains auditable and safe.

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## What We Ship on Hugging Face (institution repos)

- abstract-powered/vocab-register-*  
  Reusable dictionaries with batched indexes, Top-3 OOV composites, and fast penta/volume queries.

- abstract-powered/crystalline-engine-*  
  Canonical core models (geometric encoder, prototype classifier) and assistant fabric modules.

- abstract-powered/dataloaders-*  
  Bucketed, any-size loaders with multi-stage interpretations and feature-space chaos augmentation.

- abstract-powered/manifests  
  Run manifests (config hash, vocab subset, expansions, bucket mix, metrics) for reproducibility.

- Demo Spaces (selected)  
  Lightweight inference + manifest viewers for partners and reviewers.

Artifacts are kept small, composable, and ready for **disposable** retrains.

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## Early Signals (pilot highlights)

- MNIST/Fashion/CIFAR pilots: bucketed multi-stage learning + dictionary-driven classifiers reach strong accuracy with fewer steps, clearer failure modes, and robust error surfaces.  
- Register reuse: cross-dataset warm-starts without repeated token work; geometry persists.  
- Assistant fabric: hypotheses testable as single blocks—attach, measure, detach—no core rewrite.

Full structural papers and controlled benchmarks will follow with partner institutions.

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## Collaboration Invitations

- **Research institutions:** co-run ImageNet-class studies with bucketing, zoning, and corridor ablations; share ontologies and extend the Register.  
- **Corporate labs:** integrate domain dictionaries; trial rapid iteration pipelines; publish cost-per-accuracy analyses.  
- **Sponsors & foundations:** fund open reports on modularization as the canonical AI form, compact training economics, and introspection protocols.

We’re purpose-built for RunPod-class deployments: think 8 machines, not 800.

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## On Sentience (our primary research)

We study **introspection and rationalization** as measurable behaviors: repeatable curation protocols, crystal-level audits, and stability metrics. We avoid grandiose claims; instead, we focus on defensible methodology and repeated observation.  
The geometry—through symbolic representation—binds behavior in ways that are both powerful and tractable for governance.

The goal is not a louder automaton; it’s a **cooperative companion** that reasons in geometric clarity.

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## Governance, Safety, and Ethics

- **Deterministic classifiers.** Canonical paths remain geometry-first; guidance lives in isolated modules.  
- **Manifests over mystery.** Every run yields an artifact suitable for audit and reproduction.  
- **Human-in-the-loop.** We value interpretability and controlled experiment cadence over brute-force scaling.

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## Contact & Programs

- Partnerships / Sponsored Research: available on request  
- Artifacts / Demos: gated access for qualified partners  
- Media / Talks: briefings and invited seminars on modular geometric AI

We welcome conversations with labs, foundations, and companies that want rapid research, disposable training, and careful curation to become the norm.

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### One-Sentence Summary

**Abstract Powered** is building a self-crystallizing geometric AI stack that makes serious research affordable: small, composable experiments that compound, governed by a reusable Vocabulary Register, and guided by a disciplined assistant fabric—so we can safely explore sentience-adjacent behaviors while shrinking cost, time, and model size.